5 papers
Environment-Grounded Automated Prompt Optimization for LLM Game Agents
Rean Clive Fernandes, Lukas Fehring, Theresa Eimer +2
LLM agents in interactive environments are highly sensitive to their prompts, yet prompt engineering remains a manual, task-specific process. We introduce an automated prompt optim…
MO-CAPO: Multi-Objective Cost-Aware Prompt Optimization
Jan Büssing, Moritz Schlager, Timo Heià +2
Large language models (LLMs) achieve strong performance across a wide range of tasks but are highly sensitive to prompt design, motivating the need for automatic prompt optimizatio…
promptolution: A Unified, Modular Framework for Prompt Optimization
Tom Zehle, Timo HeiÃ, Moritz Schlager +2
Prompt optimization has become crucial for enhancing the performance of large language models (LLMs) across a broad range of tasks. Although many research papers demonstrate its ef…
Overtuning in Hyperparameter Optimization
Lennart Schneider, Bernd Bischl, Matthias Feurer
Hyperparameter optimization (HPO) aims to identify an optimal hyperparameter configuration (HPC) such that the resulting model generalizes well to unseen data. As the expected gene…
CAPO: Cost-Aware Prompt Optimization
Tom Zehle, Moritz Schlager, Timo Heià +1
Large language models (LLMs) have revolutionized natural language processing by solving a wide range of tasks simply guided by a prompt. Yet their performance is highly sensitive t…